Longwall mining “cutting cantilever beam theory” and 110 mining method in China——The third mining science innovation

With the third innovation in science and technology worldwide, China has also experienced thismarvelous progress. Concerning the longwall mining in China, the ＂masonry beam theory＂ （MBT） wasfirst proposed in the 1960s, illustrating that the transmission and equilibrium method of overburdenpressure using reserved coal pillar in mined-out areas can be realized. This forms the so-called ＂121mining method＂, which lays a solid foundation for development of mining science and technology inChina. The ＂transfer rock beam theory＂ （TRBT） proposed in the 1980s gives a further understanding forthe transmission path of stope overburden pressure and pressure distribution in high-stress areas. In thisregard, the advanced 121 mining method was proposed with smaller coal pillar for excavation design,making significant contributions to improvement of the coal recovery rate in that era. In the 21st century,the traditional mining technologies faced great challenges and, under the theoretical developmentspioneered by Profs. Minggao Qian and Zhenqi Song, the ＂cutting cantilever beam theory＂ （CCBT） wasproposed in 2008. After that the 110 mining method is formulated subsequently, namely one stope face,after the first mining cycle, needs one advanced gateway excavation, while the other one is automaticallyformed during the last mining cycle without coal pillars left in the mining area. This method can beimplemented using the CCBT by incorporating the key technologies, including the directional presplittingroof cutting, constant resistance and large deformation （CRLD） bolt/anchor supporting systemwith negative Poisson＇s ratio （NPR） effect material, and remote real-time monitoring technology. TheCCBT and 110 mining method will provide the theoretical and technical basis for the development ofmining industry in China.

Monte Carlo Analytic Hierarchy Process (MAHP) approach to selection of optimum mining method

One of the most critical and complicated steps in mine design is a selection of suitable mining method based upon geological,geotechnical,geographical,safety and economical parameters.The aim of this study is developing a Monte Carlo simulation to selection the optimum mining method by using effective and major criteria and at the same time,taking subjective judgments of decision makers into consideration.Proposed approach is based on the combination of Monte Carlo simulation with conventional Analytic Hierarchy Process(AHP).Monte Carlo simulation is used to determine the confdence level of each alternative’s score,is calculated by AHP,with the respect to the variance of decision makers’opinion.The proposed method is applied for Jajarm Bauxite Mine in Iran and eventually the most appropriate mining methods for this mine are ranked.

Research on the Permeability Characteristics of Granite with Different Weathering Degrees before and after the Influence of Mining Method Construction

The influence of mining method tunnel construction on the groundwater environment is a very important and complex engineering environment problem, and the strong differential weathering of water-rich granite strata increases the difficulty of this problem. In this paper, the mineral composition and microstructure characteristics of granite with different weathering degrees before and after the influence of mining method were studied by in-situ and indoor seepage tests and theoretical calculation, and the impact of mining method tunneling on granite permeability was also analyzed. Calculation results revealed that the permeability coefficient of surrounding rock at 1.1 m away from excavation face increased 41.6 times as much as the original. The permeability coefficient of moderately and strongly weathered granite increased by 6.12 and 3.33 times, respectively and the permeability also increased. The variation of the permeability coefficient of fully weathered granite was the smallest, increasing by 1.67 times, which is due to mechanical excavation of a fully weathered layer on-site, and the disturbance was far less than that caused by blasting. The scale of the excavation damaged zone (EDZ) induced by mining method was determined by wave velocity test, which provides a basis for subsequent seepage field calculation and research.

Numerical Simulation of the Influence of Tunnel Construction by Mining Method on the Seepage Field in Weathered Granite Stratum

In actual space, considering the heterogeneity and anisotropy of rock and soil, the difference of hydrogeological conditions and the influence of tunnel excavation, tunnel seepage problem is a very complex three-dimensional seepage problem, which is very difficult to solve. The equivalent continuum model is one of the most commonly simplified models used in solving tunnel seepage problems. In this paper, the finite element software ABAQUS and the research results are used to establish a seepage numerical calculation model, study the influence of mining method construction on the seepage field in weathered granite, and clarify the influence of each stage of mining method construction on the groundwater environment. On this basis, the sensitivity of the seepage field to various factors such as natural environment, engineering geology and hydrogeology, tunnel construction and so on is analyzed, which provides a basis to establish the evaluation system of groundwater environment negative effect in weathered granite stratum by mining method tunnel construction.

Tacit Knowledge Mining:The Key Traditional Chinese Medical Inheritance

Traditional Chinese medicine(TCM)is a treasure of traditional Chinese culture and a gift to the world.TCM tacit knowledge refers to the knowledge and experiences formed in the process of learning and practice of TCM.The objective of this study is to discuss the importance of TCM tacit knowledge in the inheritance and education of TCM.As the essence of the TCM,TCM tacit knowledge has the characteristics of massive,complicated,relativistic,highly individualized,constantly innovative,the dependence of cultural background and the regional environment,as well as difficult to explicate.It exists in every aspect of the TCM theory and the process of dialectical treatment.Besides the traditional master‑apprentice,family‑based,school‑based,and inheritance and education methods,together with the inheritance based on the books,images,and network platforms,in the process of TCM modernization,a variety of modern theoretical models and computing techniques have also been used in the mining of the TCM tacit knowledge.In this study,we introduced the usage of SECI model,complexity adaptive system,latent variable model,and some of the data mining technologies in the TCM tacit knowledge mining.An accurate and efficient inheritance of TCM tacit knowledge is the key to maintain the vitality and innovative development of TCM.Under the reasonable application and combination of the traditional education methods,modern mining methods,and further the artificial intelligence,the explicit and inheritance of TCM tacit knowledge will get tremendous development,and it could extremely improve the efficiency and accuracy of the TCM inheritance and the TCM modernization.

FARMER:A novel approach to file access correlation mining and evaluation reference model

File semantic has proven effective in optimizing large scale distributed file system.As a consequence of the elaborate and rich I/O interfaces between upper layer applications and file systems,file system can provide useful and insightful information about semantic.Hence,file semantic mining has become an increasingly important practice in both engineering and research community.Unfortunately,it is a challenge to exploit file semantic knowledge because a variety of factors coulda ffect this information exploration process.Even worse,the challenges are exacerbated due to the intricate interdependency between these factors,and make it difficult to fully exploit the potentially important correlation among various semantic knowledges.This article proposes a file access correlation miming and evaluation reference（FARMER） model,where file is treated as a multivariate vector space,and each item within the vector corresponds a separate factor of the given file.The selection of factor depends on the application,examples of factors are file path,creator and executing program.If one particular factor occurs in both files,its value is non-zero.It is clear that the extent of inter-file relationships can be measured based on the likeness of their factor values in the semantic vectors.Benefit from this model,FARMER represents files as structured vectors of identifiers,and basic vector operations can be leveraged to quantify file correlation between two file vectors.FARMER model leverages linear regression model to estimate the strength of the relationship between file correlation and a set of influencing factors so that the ＂bad knowledge＂ can be filtered out.To demonstrate the ability of new FARMER model,FARMER is incorporated into a real large-scale object-based storage system as a case study to dynamically infer file correlations.In addition FARMER-enabled optimize service for metadata prefetching algorithm and object data layout algorithm is implemented.Experimental results show that is FARMER-enabled prefetching algorithm is shown to reduce the metadata operations latency by approximately 30%-40% when compared to a state-of-the-art metadata prefetching algorithm and a commonly used replacement policy.

Text Mining Analysis of Efficiency of the Continuously Implemented Gathering Type Action Plan for Male Elderly People Obtained

Aim: To clarify transformation of the participants’ consciousness for rebuilding the community and its factors from the discussion contents by actions for male elderly people in Town A in Fukushima prefecture. Design: This study was an action research. Method: The author verbalized discussion contents of the action conducted in 2018-2019 and analyzed them for each year by the text mining method. Results: The word appearance frequency was high in the order of “Person” and “Town A” in both years. One large word network was formed in 2018 and its topic was about what the participants feel in their life in Town A. Two large word networks were formed in 2019 and their topic was about the community participation including difficulty in motivating others such as how people who do not participate can feel like joining it.

Comparative evaluation of data mining methods in predicting the water vapor permeability of cement-based materials

Water vapor permeability of building materials is a crucial parameter for analysing and optimizing the hygrothermal performance of building envelopes and built environments.Its measurement is accurate but time-consuming,while data mining methods have the potential to predict water vapor permeability efficiently.In this study,six data mining methods—support vector regression(SVR),decision tree regression(DT),random forest regression(RF),K-nearest neighbor(KNN),multi-layer perceptron(MLP),and adaptive boosting regression(AdaBoost)—were compared to predict the water vapor permeability of cement-based materials.A total of 143 datasets of material properties were collected to build prediction models,and five materials were experimentally determined for model validation.The results show that RF has excellent generalization,stability,and precision.AdaBoost has great generalization and precision,only slightly inferior to the former,and its stability is excellent.DT has good precision and acceptable generalization,but its stability is poor.SVR and KNN have superior stability,but their generalization and precision are inadequate.MLP lacks generalization,and its stability and precision are unacceptable.In short,RF has the best comprehensive performance,demonstrated by a limited prediction deviation of 26.3%from the experimental results,better than AdaBoost(38.0%)and DT(38.3%)and far better than other remaining methods.It is also found that data mining methods provide better predictions when cement-based materials’water vapor permeability is high.

Research progress and development trends of highway tunnels in China

The highway tunnel system in China has in recent years surpassed Europe, the United States, and other developed countries in terms of mileage, scale, complexity, and technical achievement. Much scientific research has been conducted, and the results have greatly facilitated the rapid development of China＇s highway tunnel building capacity. This article presents the historical development of highway tunneling in China, according to specific charac- teristics based on construction and operation. It provides a systematic analysis of the major achievements and chal- lenges with respect to construction techniques, operation, monitoring, repair, and maintenance. Together with future trends of highway tunneling in China, suggestions have been made for further research, and development prospects have been identified with the for a Chinese-style highway aim of laying the foundation tunnel construction method and technical architecture.

Study of the Distribution Patterns of the Constituent Herbs in Classical Chinese Medicine Prescriptions Treating Respiratory Disease by Data Mining Methods

Objective： To provide the distribution pattern and compatibility laws of the constituent herbs in prescriptions, for doctor＇s convenience to make decision in choosing correct herbs and prescriptions for treating respiratory disease. Methods： Classical prescriptions treating respiratory disease were selected from authoritative prescription books. Data mining methods （frequent itemsets and association rules） were used to analyze the regular patterns and compatibility laws of the constituent herbs in the selected prescriptions. Results： A total of 562 prescriptions were selected to be studied. The result exhibited that, Radix g/ycyrrhizae was the most frequently used in 47.2% prescriptions, other frequently used were Semen armeniacae amarum, Fructus schisandrae Chinese, Herba ephedrae, and Radix ginseng. Herbal ephedrae was always coupled with Semen armeniacae amarum with the confidence of 73.3%, and many herbs were always accompanied by Radix g/ycyrrhizae with high confidence. More over, Fructus schisandrae Chinese, Herba ephedrae and Rhizoma pinelliae was most commonly used to treat cough, dyspnoea and associated sputum respectively besides Radix glycyrrhizae and Semen armeniacae amarum. The prescriptions treating dyspnoea often used double herb group of Herba ephedrae ＆ Radix glycyrrhizae, while prescriptions treating sputum often used double herb group of Rhizoma pinel/iae ＆ Radix glycyrrhizae and Rhizoma pinelliae ＆ Semen armeniacae amarum, triple herb groups of Rhizoma pinelliae ＆ Semen armeniacae amarum ＆ Radix glycyrrhizae and Pericarpium citri reticu/atae ＆ Rhizoma pine/liae ＆ Radix glycyrrhizae. Couclusioas： The prescriptions treating respiratory disease showed common compatibility laws in using herbs and special compatibility laws for treating different respiratory symptoms. These principle patterns and special compatibility laws reported here could be useful for doctors to choose correct herbs and prescriptions in treating respiratory disease.

A discrete model for prediction of radon flux from fractured rocks

Prediction of radon flux from the fractured zone of a propagating cave mine is basically associated with uncertainty and complexity. For instance, there is restricted access to these zones for field measure- ments, and it is quite difficult to replicate the complex nature of both natural and induced fractures in these zones in laboratory studies. Hence, a technique for predicting radon flux from a fractured rock using a discrete fracture network （DFN） model is developed to address these difficulties. This model quantifies the contribution of fractures to the total radon flux, and estimates the fracture density from a measured radon flux considering the effects of advection, diffusion, as well as radon generation and decay. Radon generation and decay are classified as reaction processes. Therefore, the equation solved is termed as the advection-diffusion-reaction equation （ADRE）. Peclet number （Pe）, a conventional dimensionless parameter that indicates the ratio of mass transport by advection to diffusion, is used to classify the transport regimes. The results show that the proposed model effectively predicts radon flux from a fractured rock. An increase in fracture density for a rock sample with uniformly distributed radon generation rate can elevate radon flux significantly compared with another rock sample with an equivalent increase in radon generation rate. In addition to Pe, two other independent dimensionless parameters （derived for radon transport through fractures） significantly affect radon dimensionless flux. Findings provide insight into radon transport through fractured rocks and can be used to improve radon control measures for proactive mitigation.

Applying data mining techniques to classify patients with suspected hepatitis C virus infection

Background Hepatitis C virus(HCV)has a high prevalence worldwide,and the progression of the disease can cause irreversible damage to severe liver damage or even death.Therefore,developing prediction models using machine learning techniques is beneficial.This study was conducted to classify suspected patients with HCV infection using different classification models.Methods The study was conducted using a dataset derived from the University of California,Irvine(UCI)Ma-chine Learning Repository.Since the HCV dataset was imbalanced,the synthetic minority oversampling technique(SMOTE)was applied to balance the dataset.After cleaning the dataset,it was divided into training and test data for developing six classification models.These six algorithms included the support vector machine(SVM),Gaus-sian Naïve Bayes(NB),decision tree(DT),random forest(RF),logistic regression(LR),and K-nearest neighbors(KNN)algorithm.The Python programming language was used to develop the classifiers.Receiver operating characteristic curve analysis and other metrics were used to evaluate the performance of the proposed models.Results After the evaluation of the models using different metrics,the RF classifier had the best performance among the six methods.The accuracy of the RF classifier was 97.29%.Accordingly,the area under the curve(AUC)for LR,KNN,DT,SVM,Gaussian NB,and RF models were 0.921,0.963,0.953,0.972,0.896,and 0.998,respectively,RF showing the best predictive performance.Conclusion Various machine learning techniques for classifying healthy and unhealthy patients were used in this study.Additionally,the developed models might identify the stage of HCV based on trained data.

New Challenges for Biological Text-Mining in the Next Decade

The massive flow of scholarly publications from traditional paper journals to online outlets has benefited biologists because of its ease to access. However, due to the sheer volume of available biological literature, researchers are finding it increasingly difficult to locate needed information. As a result, recent biology contests, notably JNLPBA and BioCreAtIvE, have focused on evaluating various methods in which the literature may be navigated. Among these methods, text-mining technology has shown the most promise. With recent advances in text-mining technology and the fact that publishers are now making the full texts of articles available in XML format, TMSs can be adapted to accelerate literature curation, maintain the integrity of information, and ensure proper linkage of data to other resources. Even so, several new challenges have emerged in relation to full text analysis, life-science terminology, complex relation extraction, and information fusion. These challenges must be overcome in order for text-mining to be more effective. In this paper, we identify the challenges, discuss how they might be overcome, and consider the resources that may be helpful in achieving that goal.

Performance Characterization of Game Recommendation Algorithms on Online Social Network Sites

Since years, online social networks have evolved from profile and communication websites to online portals where people interact with each other, share and consume multimedia-enriched data and play different types of games. Due to the immense popularity of these online games and their huge revenue potential, the number of these games increases every day, resulting in a current offering of thousands of online social games. In this paper, the applicability of neighborhood-based collaborative filtering （CF） algorithms for the recommendation of online social games is evaluated. This evaluation is based on a large dataset of an online social gaming platform containing game ratings （explicit data） and online gaming behavior （implicit data） of millions of active users. Several similarity metrics were implemented and evaluated on the explicit data, implicit data and a combination thereof. It is shown that the neighborhood-based CF algorithms greatly outperform the content-based algorithm, currently often used on online social gaming websites. The reslflts also show that a combined approach, fie, taking into account both implicit and explicit data at the same time, yields overall good results on all evaluation metrics for all scenarios, while only slightly performing worse compared to the strengths of the explicit or implicit only approaches. The best performing algorithms have been implemented in a live setup of the online game platform.

Feature weighted clustering for user profiling

Personalization is the adaptation of the services to fit the user’s interests,characteristics and needs.The key to effective personalization is user profiling.Apart from traditional collaborative and content-based approaches,a number of classification and clustering algorithms have been used to classify user related information to create user profiles.However,they are not able to achieve accurate user profiles.In this paper,we present a new clustering algorithm,namely Multi-Dimensional Clustering(MDC),to determine user profiling.The MDC is a version of the Instance-Based Learner(IBL)algorithm that assigns weights to feature values and considers these weights for the clustering.Three feature weight methods are proposed for the MDC and,all three,have been tested and evaluated.Simulations were conducted with using two sets of user profile datasets,which are the training(includes 10,000 instances)and test(includes 1000 instances)datasets.These datasets reflect each user’s personal information,preferences and interests.Additional simulations and comparisons with existing weighted and non-weighted instance-based algorithms were carried out in order to demonstrate the performance of proposed algorithm.Experimental results using the user profile datasets demonstrate that the proposed algorithm has better clustering accuracy performance compared to other algorithms.This work is based on the doctoral thesis of the corresponding author.